Building an Advanced AI Assistant for QAPilot: Combining RAG and LLM

Introduction

This comprehensive guide details the implementation of a sophisticated AI assistant for QAPilot, combining Retrieval-Augmented Generation (RAG) with OpenAI's GPT models. The system features a hybrid architecture that leverages both pre-existing knowledge and AI capabilities to provide accurate, context-aware responses about QAPilot's mobile testing platform.

Key Features

Multi-tier retrieval system with MongoDB and JSON fallback
Context-aware AI responses using RAG
Source attribution for responses
Scalable architecture using Node.js and Express
Intelligent query processing and relevance ranking
Error handling with graceful fallbacks

Technical Architecture

1. Data Layer

javascriptCopy// MongoDB for primary storage
const db = client.db("QApilotCoreData");
const collection = db.collection("Phrases");

// JSON fallback for basic responses
const jsonData = require('./data.json');

2. Retrieval System

The RAG implementation uses a three-stage retrieval process:

javascriptCopyasync function findSimilarDocuments(query, collection) {
  // 1. Query Processing
  const keywords = query.toLowerCase().split(/\s+/);
  const regexPatterns = keywords.map(keyword => 
    new RegExp(keyword.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'), 'i')
  );

  // 2. Document Retrieval
  const documents = await collection.find({
    $or: [
      { query: { $in: regexPatterns } },
      { response: { $in: regexPatterns } }
    ]
  }).limit(3).toArray();

  // 3. Relevance Ranking
  return documents.sort((a, b) => {
    const aMatches = keywords.filter(keyword => 
      new RegExp(keyword, 'i').test(a.query) || 
      new RegExp(keyword, 'i').test(a.response)
    ).length;
    return bMatches - aMatches;
  });
}

3. Context-Aware AI Response Generation

The system uses OpenAI's GPT model with custom context injection:

javascriptCopyasync function getAIResponseWithContext(query, context) {
  const systemPrompt = `You are a helpful QA assistant for QAPilot, a mobile automation testing tool. 
  Use the following context to answer the question:
  Context: ${context}

  If the context doesn't contain relevant information, provide a general response 
  based on common mobile automation testing knowledge.`;

  const response = await axios.post(
    'https://api.openai.com/v1/chat/completions',
    {
      model: 'gpt-3.5-turbo-0125',
      messages: [
        { role: 'system', content: systemPrompt },
        { role: 'user', content: query }
      ],
      temperature: 0.7
    }
  );
  return response.data.choices[0].message.content;
}

4. Intelligent Query Processing

The system implements a fallback mechanism to ensure reliable responses:

javascriptCopyapp.post('/search', async (req, res) => {
  const query = req.body.query?.toLowerCase() || '';

  try {
    // Primary: MongoDB RAG
    const similarDocs = await findSimilarDocuments(query, collection);

    if (similarDocs.length > 0) {
      const context = similarDocs.map(doc => 
        `Question: ${doc.query}\nAnswer: ${doc.response}`
      ).join('\n\n');

      response = await getAIResponseWithContext(query, context);
      sourceMessage = 'Response generated from database context';
    } else {
      // Secondary: JSON Fallback
      const jsonResponse = await getResponseFromJSON(query);
      if (jsonResponse) {
        response = jsonResponse;
        sourceMessage = 'Response from JSON knowledge base';
      } else {
        // Tertiary: Pure LLM
        response = await getAIResponseWithContext(query, '');
        sourceMessage = 'AI-generated response';
      }
    }

    res.send({ response, source: sourceMessage });
  } catch (error) {
    console.error('Error:', error);
    res.status(500).send('An error occurred while processing your request.');
  }
});

Future Enhancements

Vector Embeddings Integration

javascriptCopyasync function getEmbeddings(text) {
  const response = await axios.post(
    'https://api.openai.com/v1/embeddings',
    {
      model: 'text-embedding-ada-002',
      input: text
    }
  );
  return response.data.embeddings[0];
}

Semantic Search Implementation

javascriptCopyasync function semanticSearch(query, collection) {
  const queryEmbedding = await getEmbeddings(query);
  return await collection.aggregate([
    {
      $search: {
        knnBeta: {
          vector: queryEmbedding,
          path: "embedding",
          k: 5
        }
      }
    }
  ]).toArray();
}

Best Practices for Deployment

Knowledge Base Management

Regularly update the MongoDB collection with new QAPilot features
Maintain consistent document structure
Include metadata for better context matching

Performance Optimization

Implement caching for frequent queries
Use connection pooling for MongoDB
Optimize regex patterns for better matching

Monitoring and Logging

Track response sources
Monitor response times
Log unsuccessful queries for knowledge base improvement

Security Considerations

Data Protection

Secure MongoDB connection string
Implement rate limiting
Validate user input

API Security

Use environment variables for sensitive data
Implement request validation
Add authentication if needed

Error Handling

Graceful fallbacks for each layer
Proper error messages
Request timeout handling

Conclusion

This implementation provides a robust foundation for an AI-powered QA assistant that combines the benefits of RAG with the capabilities of modern language models. The system's ability to provide context-aware responses while maintaining fallback options ensures reliable and accurate information delivery about QAPilot's features and capabilities.

Building an Advanced AI Assistant for QAPilot: Combining RAG and LLM Capabilities

Create a sophisticated AI assistant for your application by integrating RAG and GPT models, focusing on context-aware responses and scalable architect